What this category of data looks like
Data primarily comes from smelting process specifications, heat inspection reports, equipment operation logs, purchase and sales contracts, and safety compliance documents. These sources cover the entire production process, with varying update cadences. Process documents are updated annually or after major process adjustments. Quality inspection and operation logs are generated in real time for each production batch. Contract documents are updated per business milestones. Most documents include fixed fields: heat number, smelting duration, alloy composition percentage, equipment operating parameters, and others. Common units are degrees Celsius, megapascals, and percentages.
What constraints these characteristics impose on the vector model and indexing workflow
Multi-source heterogeneous data structures require the indexing system to support both formatted text and unstructured documents. Separate chunking rules must be designed for long-cycle process documents and real-time single-heat quality inspection records. Real-time updated production data requires the index to support incremental synchronization, preventing excessive resource consumption from full index rebuilding. Fixed business fields require metadata to be stored alongside vector indexes, ensuring subsequent recall results can link to key details such as specific heat numbers and production batches to meet traceability needs for document drafting.
Configuration guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long paragraphs of process descriptions and quality inspection data in iron and steel smelting documents, avoiding overloading single chunks |
| Overlap Length | 100–200 characters | Retains critical data associations across chunks, preventing core fields such as heat numbers and composition indicators from being split between chunks |
| Similarity Threshold | 0.75–0.85 | Matches semantic similarity of technical terms in smelting scenarios, filtering low-correlation recall results |
| Single Search Limit | Top 8 results | Controls the number of recall results, adapting to the precise reference range needed for document drafting |
maxToken | 4000 token | Limits the total length of a single round of context, avoiding exceeding the model's processing limit |
defaultToken | Calibrated via actual testing | Adapts to input length requirements of different embedding models, complying with parameter limits of models such as Doubao-embedding |
Three common configuration mistakes
- Issue: Recall results do not link to document source fields, such as empty heat number or production batch information. Cause: Metadata synchronization was not enabled in the index configuration, only text vectors were stored and business fields were not retained.
- Issue: Parameter errors occur when calling Doubao-embedding, with a prompt that the input length exceeds the limit. Cause: The
defaultTokenparameter was not configured according to the model requirements, causing the input text to exceed the maximum supported length of the model. - Issue: Duplicate data entries appear when incrementally updating the index. Cause: No unique identifier field was set, causing the same quality inspection document to be imported into the index multiple times.
How to confirm the configuration is correct
- Upload a single-heat quality inspection report, initiate a query and check if the recall results include metadata such as heat number and smelting time.
- Call the embedding model interface to verify that the input text length complies with the
maxTokenconfiguration limit. - Initiate a query related to process drafting and check that the number of recall results matches the preset single search limit.
- Perform an incremental synchronization operation and confirm that newly generated production documents are correctly added to the index with no duplicate entries.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.